arXiv subjects

Can Cui

Publications and source records attributed to Can Cui.

126 records · Page 7Linked to original sources

arXiv2021

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning. Although supervised methods have achieved satisfactory performance in VS segmentation, they require full annotations by experts, which is laborious and time-consuming. In this work, we aim to tackle the VS and cochlea segmentation problem in an unsupervised domain adaptation setting. Our proposed method leverages both the image-level domain alignment to minimize the domain divergence and semi-supervised training to further boost the performance. Furthermore, we propose to fuse the labels predicted from multiple models via noisy label correction. Our results on the challenge validation leaderboard showed that our unsupervised method has achieved promising VS and cochlea segmentation performance with mean dice score of 0.8261 $\pm$ 0.0416; The mean dice value for the tumor is 0.8302 $\pm$ 0.0772. This is comparable to the weakly-supervised based method.

eess.IV↗

arXiv2021

Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence

Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain's intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions "on the fly" to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron's state, its dynamics and its transfer function "on the fly". This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster from smaller amounts of data and using a more compact and energy-efficient network than the state-of-the-art non-adaptive ANNs used for cancer diagnosis. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modern ANNs require large amounts of training data, energy and chip area and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning from smaller datasets, compact architectures, energy-efficiency, fault-tolerance and can lead to the realization of general artificial intelligence.

physics.app-ph↗

arXiv2021

Global Three-Dimensional Simulations of Outer Protoplanetary Disks with Ambipolar Diffusion

The structure and evolution of protoplanetary disks (PPDs) are largely governed by disk angular momentum transport, mediated by magnetic fields. In the most observable outer disk, PPD gas dynamics is primarily controlled by ambipolar diffusion as the dominant non-ideal magnetohydrodynamic (MHD) effect. In this work, we study the gas dynamics in outer PPDs by conducting a set of global 3D non-ideal MHD simulations with ambipolar diffusion and net poloidal magnetic flux, using the Athena++ MHD code, with resolution comparable to local simulations. Our simulations demonstrate the co-existence of magnetized disk wind and turbulence driven by the magneto-rotational instability (MRI). While MHD wind dominates disk angular momentum transport, the MRI turbulence also contributes significantly. We observe that magnetic flux spontaneously concentrate into axisymmetric flux sheets, leading to radial variations in turbulence levels, stresses, and accretion rates. Annular substructures arise as a natural consequence of magnetic flux concentration. The flux concentration phenomena show diverse properties with different levels of disk magnetization and ambipolar diffusion. The disk generally loses magnetic flux over time, though flux sheets could prevent the leak of magnetic flux in some cases. Our results demonstrate the ubiquity of disk annular substructures in weakly MRI turbulent outer PPDs, and imply a stochastic nature of disk evolution.

astro-ph.EP↗

arXiv2021

Asynchronous Multi-View SLAM

Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing. For evaluation, we collected AMV-Bench, a challenging new SLAM dataset covering 482 km of driving recorded using our asynchronous multi-camera robotic platform. AMV-Bench is over an order of magnitude larger than previous multi-view HD outdoor SLAM datasets, and covers diverse and challenging motions and environments. Our experiments emphasize the necessity of asynchronous sensor modeling, and show that the use of multiple cameras is critical towards robust and accurate SLAM in challenging outdoor scenes. For additional information, please see the project website at: https://www.cs.toronto.edu/~ajyang/amv-slam

cs.RO↗

arXiv2021

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and 0.8594 +/- 0.0275 on zebrafish data, a dramatic improvement over existing unsupervised algorithms.

eess.IV↗

arXiv2021

Generalizing Nucleus Recognition Model in Multi-source Images via Pruning

Ki67 is a significant biomarker in the diagnosis and prognosis of cancer, whose index can be evaluated by quantifying its expression in Ki67 immunohistochemistry (IHC) stained images. However, quantitative analysis on multi-source Ki67 images is yet a challenging task in practice due to cross-domain distribution differences, which result from imaging variation, staining styles, and lesion types. Many recent studies have made some efforts on domain generalization (DG), whereas there are still some noteworthy limitations. Specifically in the case of Ki67 images, learning invariant representation is at the mercy of the insufficient number of domains and the cell categories mismatching in different domains. In this paper, we propose a novel method to improve DG by searching the domain-agnostic subnetwork in a domain merging scenario. Partial model parameters are iteratively pruned according to the domain gap, which is caused by the data converting from a single domain into merged domains during training. In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains. Furthermore, an appropriate implementation is attained by applying the pruning method to different parts of the framework. Compared with known DG methods, our method yields excellent performance in multiclass nucleus recognition of Ki67 IHC images, especially in the lost category cases. Moreover, our competitive results are also evaluated on the public dataset over the state-of-the-art DG methods.

cs.CV↗

arXiv2021

On the Vertical Shear Instability in Magnetized Protoplanetary Disks

The vertical shear instability (VSI) is a robust phenomenon in irradiated protoplanetary disks (PPDs). While there is extensive literature on the VSI in the hydrodynamic limit, PPDs are expected to be magnetized and their extremely low ionization fractions imply that non-ideal magneto-hydrodynamic (MHD) effects should be properly considered. To this end, we present linear analyses of the VSI in magnetized disks with Ohmic resistivity. We primarily consider toroidal magnetic fields, which are likely to dominate the field geometry in PPDs. We perform vertically global and radially local analyses to capture characteristic VSI modes with extended vertical structures. To focus on the effect of magnetism, we use a locally isothermal equation of state. We find that magnetism provides a stabilizing effect to dampen the VSI, with surface modes, rather than body modes, being the first to vanish with increasing magnetization. Subdued VSI modes can be revived by Ohmic resistivity, where sufficient magnetic diffusion overcome magnetic stabilization, and hydrodynamic results are recovered. We also briefly consider poloidal fields to account for the magnetorotational instability (MRI), which may develop towards surface layers in the outer parts of PPDs. The MRI grows efficiently at small radial wavenumbers, in contrast to the VSI. When resistivity is considered, we find the VSI dominates over the MRI for Ohmic Elsässer numbers $\lesssim 0.09$ at plasma beta parameter $β_Z \sim 10^4$.

astro-ph.EP↗

arXiv2021

AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment

Alphas are stock prediction models capturing trading signals in a stock market. A set of effective alphas can generate weakly correlated high returns to diversify the risk. Existing alphas can be categorized into two classes: Formulaic alphas are simple algebraic expressions of scalar features, and thus can generalize well and be mined into a weakly correlated set. Machine learning alphas are data-driven models over vector and matrix features. They are more predictive than formulaic alphas, but are too complex to mine into a weakly correlated set. In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes. The new alphas predict returns with high accuracy and can be mined into a weakly correlated set. In addition, we propose a novel alpha mining framework based on AutoML, called AlphaEvolve, to generate the new alphas. To this end, we first propose operators for generating the new alphas and selectively injecting relational domain knowledge to model the relations between stocks. We then accelerate the alpha mining by proposing a pruning technique for redundant alphas. Experiments show that AlphaEvolve can evolve initial alphas into the new alphas with high returns and weak correlations.

cs.AI↗

arXiv2021

Skyrmion Logic Clocked via Voltage Controlled Magnetic Anisotropy

Magnetic skyrmions are exciting candidates for energy-efficient computing due to their non-volatility, detectability,and mobility. A recent proposal within the paradigm of reversible computing enables large-scale circuits composed ofdirectly-cascaded skyrmion logic gates, but it is limited by the manufacturing difficulty and energy costs associated withthe use of notches for skyrmion synchronization. To overcome these challenges, we therefore propose a skyrmion logicsynchronized via modulation of voltage-controlled magnetic anisotropy (VCMA). In addition to demonstrating theprinciple of VCMA synchronization through micromagnetic simulations, we also quantify the impacts of current den-sity, skyrmion velocity, and anisotropy barrier height on skyrmion motion. Further micromagnetic results demonstratethe feasibility of cascaded logic circuits in which VCMA synchronizers enable clocking and pipelining, illustrating afeasible pathway toward energy-efficient large-scale computing systems based on magnetic skyrmions.

cond-mat.mes-hall↗

arXiv2020

Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions

Complementary metal oxide semiconductor (CMOS) devices display volatile characteristics, and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well-suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications. However, a large quantity of these artificial neuromorphic devices still require the use of CMOS, which decreases the efficiency of the system. To resolve this, we have previously proposed a number of artificial neurons and synapses that do not require CMOS for operation. Although these devices are a significant improvement over previous renditions, their ability to enable neural network learning and recognition is limited by their intrinsic activation functions. This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track. Linear and sigmoidal activation functions are demonstrated in this work, which can be extended through a similar approach to enable a wide variety of activation functions.

cs.NE↗

arXiv2020

Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation

Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS), but these are known to lack robustness when anatomic differences between atlases and subjects are large. To improve the localization robustness, we propose a novel two-stage deep learning (DL) framework, where the first stage identifies and crops the thalamus regions from the whole brain MRI and the second stage performs per-voxel regression on the cropped volume to localize the targets at the finest resolution scale. To address the issue of data scarcity, we train the models with the pseudo labels which are created based on the available labeled data using multi-atlas registration. To assess the performance of the proposed framework, we validate two sampling-based uncertainty estimation techniques namely Monte Carlo Dropout (MCDO) and Test-Time Augmentation (TTA) on the second-stage localization network. Moreover, we propose a novel uncertainty estimation metric called maximum activation dispersion (MAD) to estimate the image-wise uncertainty for localization tasks. Our results show that the proposed method achieved more robust localization performance than the traditional multi-atlas method and TTA could further improve the robustness. Moreover, the epistemic and hybrid uncertainty estimated by MAD could be used to detect the unreliable localizations and the magnitude of the uncertainty estimated by MAD could reflect the degree of unreliability for the rejected predictions.

eess.IV↗

arXiv2020

A Novel Semi-Supervised Data-Driven Method for Chiller Fault Diagnosis with Unlabeled Data

In practical chiller systems, applying efficient fault diagnosis techniques can significantly reduce energy consumption and improve energy efficiency of buildings. The success of the existing methods for fault diagnosis of chillers relies on the condition that sufficient labeled data are available for training. However, label acquisition is laborious and costly in practice. Usually, the number of labeled data is limited and most data available are unlabeled. The existing methods cannot exploit the information contained in unlabeled data, which significantly limits the improvement of fault diagnosis performance in chiller systems. To make effective use of unlabeled data to further improve fault diagnosis performance and reduce the dependency on labeled data, we proposed a novel semi-supervised data-driven fault diagnosis method for chiller systems based on the semi-generative adversarial network, which incorporates both unlabeled and labeled data into learning process. The semi-generative adversarial network can learn the information of data distribution from unlabeled data and this information can help to significantly improve the diagnostic performance. Experimental results demonstrate the effectiveness of the proposed method. Under the scenario that there are only 80 labeled samples and 16000 unlabeled samples, the proposed method can improve the diagnostic accuracy to 84%, while the supervised baseline methods only reach the accuracy of 65% at most. Besides, the minimal required number of labeled samples can be reduced by about 60% with the proposed method when there are enough unlabeled samples.

cs.LG↗

arXiv2020

Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices and Circuits for In-Memory Computing

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction (MTJ) memory elements can be used for computation by manipulating a domain wall (DW), a transition region between magnetic domains. But, these devices have suffered from challenges: spin transfer torque (STT) switching of a DW requires high current, and the multiple etch steps needed to create an MTJ pillar on top of a DW track has led to reduced tunnel magnetoresistance (TMR). These issues have limited experimental study of devices and circuits. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction (DW-MTJ) in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy (PMA), spin-orbit torque (SOT) switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 164%, resistance-area product RA = 31 Ω-μm^2, close to the RA of the unpatterned film, and lower switching current density compared to using spin transfer torque. A two-device circuit shows bit propagation between devices. Device initialization variation in switching voltage is shown to be curtailed to 7% by controlling the DW initial position, which we show corresponds to 96% accuracy in a DW-MTJ full adder simulation. These results make strides in using MTJs and DWs for in-memory and neuromorphic computing applications.

cond-mat.mes-hall↗

arXiv2020

Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical properties of this device and optimal implementation of neuroscience-inspired plasticity learning rules, and highlight performance on a suite of tasks. Our energy analysis confirms the value of the approach, as the learning budget stays below 20 $μJ$ even for large tasks used typically in machine learning.

cs.NE↗

arXiv2020

Large-scale dynamics of winds originated from black hole accretion flows: (I) Hydrodynamics

Winds from black hole accretion flows are ubiquitous. Previous works mainly focus on the launching of wind in the accretion flow scale. It still remains unclear how far the winds can propagate outward and what is their large-scale dynamics. As the first paper of this series, we study the large-scale dynamics of thermal wind beyond accretion scales via analytical and numerical methods. Boundary conditions, which are crucial to our problem, are analyzed and presented based on the small-scale simulations combined with observations of winds. Both black hole and galaxy potential are taken into account. For winds originated from hot accretion flows, we find that the wind can reach to large scales. The radial profiles of velocity, density, and temperature can be approximated by $v_r\approx v_{r0}, ρ\approx ρ_{0}(r/r_0)^{-2}$, and $T\approx T_0 (r/r_0)^{-2(γ-1)}$, where $v_{r0}, ρ_0, T_0$ are the velocity, density, and temperature of winds at the boundary $r_0(\equiv 10^3 r_g)$, $γ$ is the polytropic index. During the outward propagation, the enthalpy and the rotational energy compensate the increase of gravitational potential. For thin disks, we find that because the Bernoulli parameter is smaller, winds cannot propagate as far as the hot winds, but stop at a certain radius where the Bernoulli parameter is equal to the potential energy. Before the winds stop, the profiles of dynamical quantities can also be approximated by the above relations. In this case the rotational energy alone compensates the increase of the potential energy.

astro-ph.HE↗

arXiv2020

Large-scale dynamics of winds originated from black hole accretion flows: (II) Magnetohydrodynamics

Winds from black hole accretion disks are essential ingredients in understanding the coevolution between the supermassive black hole and its host galaxy. The great difference of dynamical ranges from small-scale accretion disk simulations to large-scale or cosmological simulations places barriers to track wind kinematics. In the first paper of this series, we have studied the dynamics of disk winds from the outer edge of the accretion disk toward galaxy scales in the hydrodynamical framework. In this paper, we further incorporate magnetic fields to understand the wind dynamics by adopting one-dimensional magnetohydrodynamical (MHD) model, with boundary conditions set for hot accretion flows. The geometry of poloidal magnetic field is prescribed as a straight line with an angle $θ=45^\circ$ from the rotational axis, and the strength satisfies the divergence free condition. The wind solution is achieved through requesting gas to pass through the slow, Alfvén and fast magneto-sonic points smoothly. Physical quantities are found to show a power-law dependence on cylindrical radius $R$ beyond the fast magneto-sonic point, for which $ρ\propto R^{-2}, v_{\rm p}\propto {\rm const.}, v_{\rm ϕ}\propto R^{-1}, B_{\rm ϕ}\propto R^{-1},$ and $ β\propto ρ^{γ-1}$. The magnetization of wind is dominant in determining the wind properties. The wind is accelerated to a greater terminal velocity with strong magnetization ($v_{\rm Ap0}>1$) compared to the hydrodynamical case, which the magnetic pressure gradient dominates and the centrifugal potential converts to the kinetic energy. The dependance of wind physical quantities on magnetization, temperature, field line angular velocity, and adiabatic index is also discussed.

astro-ph.HE↗

arXiv2020

Global Simulations of the Vertical Shear Instability with Non-ideal Magnetohydrodynamical Effects

The mechanisms of angular momentum transport and level of turbulence in protoplanetary disks (PPDs) are crucial for understanding many aspects of planet formation. In the recent years, it has been realized that the magneto-rotational instability (MRI) tends to be suppressed in PPDs due to non-ideal MHD effects, and the disk is largely laminar with accretion driven by magnetized disk winds. In parallel, several hydrodynamical mechanisms have been identified that likely also generate vigorous turbulence and drive disk accretion. We study the interplay between MHD winds in PPDs with the vertical shear instability (VSI), one of the most promising hydrodynamical mechanisms, through 2D global non-ideal MHD simulations with ambipolar diffusion and Ohmic resistivity. We find that for typical disk parameters, MHD winds can coexist with the VSI with accretion primarily wind-driven accompanied by vigorous VSI turbulence. The properties of the VSI remain similar to unmagnetized case, and the wind and overall field configuration are not strongly affected by VSI turbulence, showing modest level of variability and corrugation of midplane current sheet. Enhanced coupling between gas and magnetic field weakens the VSI. The VSI is also weakened with increasing magnetization, and we find that corrugation motions characteristic of the VSI transitions to low-amplitude breathing mode oscillations.

astro-ph.EP↗

arXiv2018

Chandra Survey of Nearby Galaxies: Testing the Accretion Model for Low-luminosity AGNs

From a Chandra sample of active galactic nuclei (AGNs) in nearby galaxies, we find that for low-luminosity AGNs (LLAGNs), either the intrinsic absorption column density, or the fraction of absorbed AGNs, positively scales with the Eddington ratio for $L_{\rm bol}/L_{\rm Edd} \lesssim 10^{-2}$. Such a behavior, along with the softness of the X-ray spectrum at low luminosities, is in good agreement with the picture that they are powered by hot accretion flows surrounding supermassive black holes. Numerical simulations find that outflows are inevitable with hot accretion flows, and the outflow rate is correlated with the innermost accretion rate in the low-luminosity regime. This agrees well with our results, suggesting that the X-ray absorption originates from or is associated with the outflow material. Gas and dust on larger scales may also produce the observed correlation. Future correlation analysis may help differentiate the two scenarios.

astro-ph.HE↗